How does the human brain acquire intelligence? Neural systems in the brain have various functions by forming very complex networks, and much remains a mystery. One approach to this question is to use mathematical models that represent the dynamics of biological neurons from a microscopic chemical viewpoint. You can simulate and analyze spiking phenomena, and investigate the functions of the biological neural system that the model mimics. These models, which originated in the Hodgkin-Huxley model [1]} and the FitzHugh-Nagumo model [2]}, [3]}, can reproduce various types of neural behavior. Because of their complexity, however, it is difficult to analyze the neural networks composed by these neuron models. Therefore, motivated by the desire to understand the efficient computational architecture underlying biological neural networks, spiking neural networks (SNNs), which are constructed with neuron models simplified for computation, have been researched. Among them, the leaky integrated firing (LIF) neuron model [4]} and its variants have been widely studied due to their mathematical tractability. SNNs are biologically plausible neural networks and differ significantly from general artificial neural networks (ANNs) in that they encode information into spikes and propagate the spikes through synapses. We focus on the dynamic temporal coding properties specific to neural circuits and SNNs in terms of the encoding scheme and the learning mechanism of the neural system. In the neural networks of biological organisms, it was shown experimentally that not only the spike firing rate but also the individual precise spike timing held information, and that it was an important factor in achieving more efficient computation [5]}, [6]}. In recent years, gradient methods for end-to-end supervised learning via direct spiking have been becoming established, but a number of research challenges remain due to the difficulties specific to SNNs.
